nep-mst New Economics Papers
on Market Microstructure
Issue of 2016‒04‒09
eight papers chosen by
Thanos Verousis

  1. Anomalous Trading Prior to Lehman Brothers' Failure By Gehrig, Thomas; Haas, Marlene
  2. Dealer Trading at the Fix By Carol Osler; ;
  3. Information Asymmetry and Information Dissemination in High-Frequency Capital Markets By Pöppe, Thomas
  4. Simple Nonparametric Estimators for the Bid-Ask Spread in the Roll Model By Xiaohong Chen; Oliver Linton; Stefan Schneeberger; Yanping Yi
  5. Estimating the Spot Covariation of Asset Prices – Statistical Theory and Empirical Evidence By Markus Bibinger; Nikolaus Hautsch; Peter Malec; Markus Reiss
  6. The German equity trading landscape By Gomber, Peter
  7. Thinning Markets in U.S. Agriculture By Adjemian, Michael; Brorsen, B. Wade; Hahn, William; Saitone, Tina L.; Sexton, Richard J.
  8. Microstructure under the Microscope: Combining Dimension Reduction, Distance Measures and Covariance By Ravi Kashyap

  1. By: Gehrig, Thomas; Haas, Marlene
    Abstract: We study price discovery during the liquidity freeze of September 2008, when fundamental values were difficult to be assessed. We find that trading volume and trade size significantly increased two days before the public announcement of Lehman's lethal quarter loss. Nevertheless, informational risk as perceived by liquidity suppliers increased only after the public disclosure of this loss. The price impact of trades was minimal and stock markets kept on working efficiently for Lehman stocks until the insolvency announcement. Price efficiency is on average established after half a second, which could have been exploited by low-latency traders.
    Keywords: low latency trading; price discovery; price impact; trading volume
    JEL: G00 G14 N00 N2
    Date: 2016–03
  2. By: Carol Osler (Brandeis University); ;
    Abstract: This paper analyzes dealer trading at "fixes," which are benchmark financial prices set at specific times of day. Extreme returns and quick retracements are common around fixes and often prompt suspicions of collusion and market manipulation, but the connections between price dynamics and dealer behavior are poorly understood. I examine a model of trading at the fix in which dealers can engage in three prohibited behaviors: front-running, sharing information about customer orders, and colluding. The model shows that dealers will engage a strategy akin to front-running regardless of whether they compete or collude, causing quick retracements after the fix. Collusion shuts down free-riding among dealers while information sharing intensifies it. Therefore collusion intensifies, and information-sharing reduces, pre-fix volatility, post-fix retracements, and the convexity of the pre-fix price path.
    Date: 2016–03
  3. By: Pöppe, Thomas
    Abstract: This dissertation is concerned with information asymmetry and information dissemination in high-frequency capital markets. At the intersection of information dissemination and asymmetry with market microstructure, this dissertation pursues three major goals. We propose enhancements to market microstructure methodology to be able to empirically conduct research on information dissemination and asymmetry on recent, high-frequency trading data. Second, we empirically evaluate related microstructure methodology to test its robustness and guide researchers in its application. Third, we employ the proposed methodology to evaluate the efficacy of different information channels, both traditional, legislation-based and new, technology-based channels.
    Date: 2016
  4. By: Xiaohong Chen (Cowles Foundation, Yale University); Oliver Linton (University of Cambridge); Stefan Schneeberger (Dept. of Economics, Yale University); Yanping Yi (Shanghai University of Finance and Economics - School of Economics)
    Abstract: We propose new methods for estimating the bid-ask spread from observed transaction prices alone. Our methods are based on the empirical characteristic function instead of the sample autocovariance function like the method of Roll (1984). As in Roll (1984), we have a closed form expression for the spread, but this is only based on a limited amount of the model-implied identification restrictions. We also provide methods that take account of more identification information. We compare our methods theoretically and numerically with the Roll method as well as with its best known competitor, the Hasbrouck (2004) method, which uses a Bayesian Gibbs methodology under a Gaussian assumption. Our estimators are competitive with Roll's and Hasbrouck's when the latent true fundamental return distribution is Gaussian, and perform much better when this distribution is far from Gaussian. Our methods are applied to the E-mini futures contract on the S&P 500 during the Flash Crash of May 6, 2010. Extensions to models allowing for unbalanced order flow or Hidden Markov trade direction indicators or trade direction indicators having general asymmetric support or adverse selection are also presented, without requiring additional data.
    Keywords: Characteristic function, Deconvolution, Flash Crash, Liquidity
    JEL: C30 C32 G10
    Date: 2016–03
  5. By: Markus Bibinger; Nikolaus Hautsch; Peter Malec; Markus Reiss
    Abstract: We propose a new estimator for the spot covariance matrix of a multi-dimensional continuous semimartingale log asset price process which is subject to noise and non-synchronous observations. The estimator is constructed based on a local average of block-wise parametric spectral covariance estimates. The latter originate from a local method of moments (LMM) which recently has been introduced by Bibinger et al. (2014). We extend the LMM estimator to allow for autocorrelated noise and propose a method to adaptively infer the autocorrelations from the data. We prove the consistency and asymptotic normality of the proposed spot covariance estimator. Based on extensive simulations we provide empirical guidance on the optimal implementation of the estimator and apply it to high-frequency data of a cross-section of NASDAQ blue chip stocks. Employing the estimator to estimate spot covariances, correlations and betas in normal but also extreme-event periods yields novel insights into intraday covariance and correlation dynamics. We show that intraday (co-)variations (i) follow underlying periodicity patterns, (ii) reveal substantial intraday variability associated with (co-)variation risk, (iii) are strongly serially correlated, and (iv) can increase strongly and nearly instantaneously if new information arrives.
    Keywords: local method of moments, spot covariance, smoothing, intraday (co-)variation risk
    JEL: C58 C14 C32
    Date: 2014–10–07
  6. By: Gomber, Peter
    Abstract: This paper describes cash equity markets in Germany and their evolution against the background of technological and regulatory transformation. The development of these secondary markets in the largest economy in Europe is first briefly outlined from a historical perspective. This serves as the basis for the description of the most important trading system for German equities, the Xetra trading system of Deutsche Börse AG. Then, the most important regulatory change for European and German equity markets in the last ten years is illustrated: the introduction of the Markets in Financial Instruments Directive (MiFID) in 2007. Its implications on equity trading in Germany are analyzed against the background of the current status of competition in Europe. Recent developments in European equity markets like the emergence of dark pools and algorithmic / high frequency trading are portrayed, before an outlook on new regulations (MiFID II, MiFIR) that will likely come into force in early 2018 will close the paper.
    Keywords: MiFID II,MiFIR,equity trading,electronic trading,cash equity markets
    Date: 2016
  7. By: Adjemian, Michael; Brorsen, B. Wade; Hahn, William; Saitone, Tina L.; Sexton, Richard J.
    Abstract: Concentration levels in U.S. agriculture are high and rising. As downstream competition declines, marketing opportunities for producers are constrained to—in some cases—a single buyer. Processors in thin markets (those with few purchasers, low trading volume, and low liquidity) could use informational advantages to depress farm-level prices for commodities (compared to a competitive market). Moreover, the low volume of trading in thin markets makes it difficult for participants and observers to gather market information and assess market performance. At the same time, many markets are moving away from traditional cash markets to bilateral contracts and vertical integration, which offer more opportunities for coordination and may foster efficiency gains that ultimately benefit producers. Both methods resolve information problems not addressed by the cash market, and forward-looking processors in many thin markets pay producers high enough prices to ensure a stable input supply. Thin market producers who can successfully enter and maintain contracts with these processors can achieve returns that meet or exceed their longrun costs. Attempting to impose greater competition on naturally thin markets can have adverse consequences for producers, processors, and consumers. However, small producers face new challenges in a thin market environment.
    Keywords: Thin markets, farm prices, competition, coordination, market power, contracts, Agribusiness, Crop Production/Industries, Industrial Organization, Livestock Production/Industries, Marketing,
    Date: 2016–03
  8. By: Ravi Kashyap
    Abstract: Market Microstructure is the investigation of the process and protocols that govern the exchange of assets with the objective of reducing frictions that can impede the transfer. In financial markets, where there is an abundance of recorded information, this translates to the study of the dynamic relationships between observed variables, such as price, volume and spread, and hidden constituents, such as transaction costs and volatility, that hold sway over the efficient functioning of the system. We consider a measure of similarity, the Bhattacharyya distance, across distributions of these variables. We illustrate a novel methodology based on the marriage between the Bhattacharyya distance and the Johnson Lindenstrauss Lemma, a technique for dimension reduction, providing us with a simple yet powerful tool that allows comparisons between data-sets representing any two distributions. We demonstrate a relationship between covariance and distance measures based on a generic extension of Stein's Lemma. The degree to which different markets or sub groups of securities have different measures of their corresponding distributions tells us the extent to which they are different. This can aid investors looking for diversification or looking for more of the same thing. We briefly discuss how this methodology lends itself to numerous Marketstructure studies and even applications outside the realm of finance / social sciences.
    Date: 2016–03

This nep-mst issue is ©2016 by Thanos Verousis. It is provided as is without any express or implied warranty. It may be freely redistributed in whole or in part for any purpose. If distributed in part, please include this notice.
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